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Digital Pathology Podcast

Digital Pathology Podcast

Von: Aleksandra Zuraw DVM PhD
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Aleksandra Zuraw from Digital Pathology Place discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.© 2026 Digital Pathology Podcast Hygiene & gesundes Leben Wissenschaft
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  • 242: How to Teach AI to Healthcare Professionals | Podcast with Candice Chu

    242: How to Teach AI to Healthcare Professionals | Podcast with Candice Chu
    Jul 2 2026
    Send us Fan MailWhat does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract?In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up.Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked together before on digital pathology and image analysis projects, so this conversation felt especially grounded. We talk about her AI literacy curriculum framework for veterinary education, why she decided to build it, and what it takes to teach AI in a way that is useful, ethical, and realistic.This episode is about understanding what AI tools are good for, where they can waste your time, and why hands-on experience matters. Candice explains why she sees AI as a set of tools, not a belief system. Try them. Learn them. Keep what improves your workflow. Drop what does not.We also talk about the difference between putting educational content online and building formal institutional teaching. That matters because social media can move quickly, but curriculum changes, research, and professional organizations shape longer-term adoption. Candice shares how her course started as a low-stakes elective, then grew into a more structured framework that combines education with publishable research.A big part of this conversation is the curriculum itself. We go through what students actually learn: AI fundamentals without heavy math, machine learning and image analysis, large language models, prompt engineering, chatbot building, ethics, literature research, and final projects where students evaluate real tools and workflows. I liked that the course does not stop at theory. It asks students to use tools, question them, and explain where they help and where they do not.We also get into something that matters far beyond veterinary medicine: professional responsibility. If AI is involved in a workflow, the clinician is still responsible. That includes fabricated citations, bad outputs, weak prompts, and the temptation to trust tools too quickly. Candice makes a strong case that AI education needs ethics, legal context, and interdisciplinary teaching built in from the start.If you are trying to think more clearly about AI in pathology, education, workflow design, or professional training, this episode gives you a concrete example of what responsible AI literacy can look like.Episode Highlights00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to02:33 – Why going back to Texas A&M changed the scale of Candice’s AI research and teaching07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects24:16 – Why ethics and legal responsibility are not optional in AI education31:35 – Why no-code tools and vibe coding are entering the curriculum already38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and PerplexityResources mentionedCandice Chu’s AI literacy curriculum framework paper in Frontiers in Veterinary ScienceCandice’s earlier work on ChatGPT in veterinary medicineTexas A&M and the institutional setting where Candice is building AI research and teachingMr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal contextClaude, Codex, and Perplexity as AI tools Candice is actively testingDigital Pathology 101, mentioned in the conversation as a teaching resourceCandice’s online educational work on Instagram.Support the showGet the "Digital Pathology 101" FREE E-book and join us!
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    42 Min.
  • 241: Foundation Models in Pathology: Strong on Paper, Ready for Labs?

    241: Foundation Models in Pathology: Strong on Paper, Ready for Labs?
    Jun 24 2026
    Send us Fan MailAre pathology foundation models actually ready for labs, or are they still stronger on paper than in practice?In this episode of DigiPath Digest #49, I unpack a timely review on pathology foundation models and ask the question that matters most to me: not just what these models can do, but what has to be true before they are genuinely useful in real pathology workflows.I walk through how pathology AI moved from narrow, task-specific models into the era of transformer-based foundation models. That shift matters because pathology is no longer only about looking at H&E in isolation. Today, pathologists are expected to integrate morphology, immunohistochemistry, molecular assays, genomics, and clinical context. That growing complexity is one reason foundation models are getting so much attention.In this discussion, I explain how transformers entered pathology, why image patches are treated like tokens, and how shared embeddings can support classification, regression, segmentation, and multimodal retrieval. I also go through the major pathology foundation models mentioned in the paper, including Virchow/Virchow2, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, GigaPath, and TITAN, and why scale alone is not the full story.A big part of this episode is about the gap between benchmark performance and clinical readiness. I talk about the persistent limitations in training data diversity, the overuse of TCGA, and why public benchmarks can still miss what real pathology practice looks like. I also cover where foundation models still struggle, especially in cytopathology, hematopathology, and underrepresented disease areas, along with the real-world problems of artifacts, domain shift, concept drift, infrastructure burden, regulatory complexity, and workflow disruption.For me, one of the most important themes is this: AI in pathology should augment, not replace, pathologists. The future is not about handing diagnosis to a model. It is about building tools that support pathologists better, fit real workflows, and can be validated in ways that deserve trust.I also spend time on what comes next: explainable AI, counterfactual explanations, conversational interfaces, retrieval-augmented systems, multimodal fusion, and the need for deployment-centric validation rather than paper-only excitement.If you are trying to understand where pathology foundation models really stand today, this episode will help you separate the promise from the practical barriers.Episode Highlights00:01 – Why I chose this paper, what is changing at Digital Pathology Place, and why foundation models are worth paying attention to now.02:15 – The core questions: what pathology foundation models are, where they are, and how difficult they are to apply in pathology.04:50 – Why pathology is becoming more cognitively demanding, and how multimodal complexity is driving interest in scalable AI.07:02 – From narrow AI to transformers: how pathology moved beyond single-task CNN models.10:16 – How transformers work in pathology: image patches as tokens, self-attention, embeddings, and downstream tasks.14:16 – Why multimodality matters, and what kinds of data foundation models may eventually integrate.15:27 – Timeline of key model developments, from “Attention Is All You Need” to gigapixel-scale pathology foundation models.17:13 – The leading models and what scale really looks like: Virchow, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, and GigaPath.19:51 – Why dataset diversity matters more than sheer volume, and why TCGA is not enough.23:17 – Where foundation models still struggle: cytopathology, hematopathology, rare disease, artifacts, scanner shifts, and pen marks.28:06 – Explainability, counterfactual explanations, and why trust in pathology AI needs more than attention maps.30:17 – The real deployment hurdles: regulation, infrastructure, workflow fit, and economics.36:32 – Why AI should augment pathologists, not replace them, and which tedious tasks pathologists would gladly hand over.38:36 – Retrieval-augmented and conversational AI in pathology: where interactive systems may actually help.40:51 – Vision-language models and multimodal fusion with histology, radiology, genomics, and clinical notes.42:16 – The path forward: deployment-centric design, prospective multi-site validation, and human-AI collaboration.44:08 – Closing thoughts on AI literacy, community learning, and what needs to happen next.Resources MentionedMain paper discussed:Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspectivehttps://doi.org/10.3390/bioengineering13050577Review article / journal landing page:https://doi.org/10.3390/bioengineering13050577Benchmarks mentioned:PathoBench — discussed in the review paper; use the review link here for context until you want to swap in a canonical project page:https://doi.org/10.3390/bioengineering13050577PathBench — ...
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    42 Min.
  • 240: AI-Powered Companion Diagnostics: The Future of Precision Medicine | Podcast with Doug Bowman, VP Precision Medicine at Indica Labs, Inc.

    240: AI-Powered Companion Diagnostics: The Future of Precision Medicine | Podcast with Doug Bowman, VP Precision Medicine at Indica Labs, Inc.
    Jun 17 2026
    Send us Fan MailHow far can pathologists take visual biomarker scoring before human vision becomes the bottleneck?In this episode, I talk with Doug Bowman. PhD, VP Precision Medicine at Indica Labs, about what happens when companion diagnostics move from traditional visual scoring into the era of AI-powered image analysis. Doug comes from a biomedical and electrical engineering background, with experience in microscopy, digital image analysis, pharma workflows, and now precision medicine at Indica Labs. That combination makes him a great person to talk to about how image analysis actually fits into real companion diagnostic development.We start with a very practical question: what is a companion diagnostic, and why is it becoming so important in precision medicine? Doug explains that companion diagnostics are developed alongside therapeutics to help identify which patients are most likely to benefit from a specific treatment, especially in more complex therapies like antibody-drug conjugates (ADCs). We use HER2 as an example, and from there we get into the real challenge: once a biomarker cutoff matters clinically, visual estimation around that cutoff becomes much harder than many people want to admit.That is where this conversation gets especially useful for pathologists and digital pathology trailblazers. We talk about the limits of human vision, why low or ultra-low biomarker expression is difficult to score consistently, and how AI helps at multiple levels of the workflow: slide QC, tissue classification, cell segmentation, membrane and cytoplasmic measurement, and spatial analysis. Doug makes the case that AI is not only a convenience here. In some cases, it is the only realistic way to capture the kind of quantitative information modern therapies need.We also get into one of the more interesting examples from the episode: the Trop2 story, where a ratio of cytoplasmic to membrane expression appears to predict therapeutic efficacy better than looking at one compartment alone. That kind of compartment-level quantitation is exactly where computational pathology becomes more than a digital version of what the eye already does. It starts uncovering measurements and signatures the eye cannot reliably extract on its own.Another important part of the discussion is workflow and regulation. Doug walks through how AI-powered companion diagnostics are developed from preclinical work, to human feasibility studies, to RUO or clinical trial assays, and eventually toward analytical and clinical validation with regulatory engagement happening early. We also talk about the Indica Labs and Leica Biosystems partnership, and why end-to-end capability matters when you are trying to build something clinically deployable rather than just analytically interesting.What I liked about this conversation is that it stayed grounded. We did not talk about AI as magic. We talked about image analysis as a method, companion diagnostics as a workflow, and precision medicine as something that only works when the measurement is good enough to support real decisions.Episode Highlights00:00 – Why AI matters in slide QC, tissue classification, and cell-level analysis before you even get to the biomarker score.00:54 – Doug Bowman’s background in biomedical engineering, microscopy, and digital image analysis.05:16 – What a companion diagnostic actually is, and why it is critical for targeted therapies and ADCs.07:34 – Why visual biomarker scoring becomes unreliable around critical cutoffs, especially in low-expression cases.10:09 – How AI expands the workflow: slide QC, tissue classification, and precise cell segmentation.13:07 – Why pathologists remain central in AI workflows through validation, markup review, and model refinement.16:31 – The Trop2 example: when cytoplasmic-to-membrane ratio tells you more than one compartment alone.20:23 – The Indica Labs + Leica Biosystems partnership and why end-to-end workflow matters in companion diagnostics.22:53 – What the development journey looks like from early algorithm work to RUO, validation, and regulatory interaction.26:51 – Multiplexing, spatial analysis, and why more clinical value often comes with more deployment complexity.33:29 – Why image analysis literacy matters, and how shared language between pathologists and scientists becomes essential.40:13 – Where to learn more about Indica Labs and who to contact for collaboration.Resources mentionedIndica Labs Indica Labs contact – info@indicalab.comHALO software / HALO AI diagnostic image analysis – discussed in the context of companion diagnostic deployment and pharma services.Leica Biosystems GT450DX – referenced as an FDA-cleared slide scanner in the Indica-Leica partnership.Digital Pathology Association – mentioned as part of the broader educational ecosystem for digital pathology and image analysis.Digital Pathology Place / Digital Pathology Podcast – the platform hosting this conversation and ...
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    41 Min.
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